import pandas as pd
import plotly.graph_objects as go
import numpy as np
import plotly.express as px
import matplotlib.pyplot as plt 

graph = pd.read_csv('icalp.lg')

graph.columns = ['node1', 'node2', 'u', 'graph']

graph

	node1	node2	u	graph
0	2	3	NaN	NaN
1	2	91	NaN	NaN
2	2	194	NaN	NaN
3	3	91	NaN	NaN
4	3	115	NaN	NaN
...	...	...	...	...
8839	4660	4662	NaN	NaN
8840	4661	4662	NaN	NaN
8841	4663	4664	NaN	NaN
8842	4666	4667	NaN	NaN
8843	4668	4669	NaN	NaN

8844 rows × 4 columns

graph = pd.read_csv('icalp.tg', header=None, sep = ' ')

graph.columns = ['node1', 'node2', 'year']

graph.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10173 entries, 0 to 10172
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype
---  ------  --------------  -----
 0   node1   10173 non-null  int64
 1   node2   10173 non-null  int64
 2   year    10173 non-null  int64
dtypes: int64(3)
memory usage: 238.6 KB

graph_new = graph.drop_duplicates()
graph_new.reset_index(drop = True, inplace = True)
graph_new

	node1	node2	year
0	2	3	1972
1	4	5	1972
2	4	6	1972
3	5	6	1972
4	8	9	1972
...	...	...	...
9944	4006	4668	2021
9945	4006	4669	2021
9946	4158	4462	2021
9947	2660	4462	2021
9948	2660	4158	2021

9949 rows × 3 columns

years = graph_new.year.unique()
print(len(years), years)

48 [1972 1974 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987
 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
 2016 2017 2018 2019 2020 2021]

years

array([1972, 1974, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984,
       1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995,
       1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
       2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
       2018, 2019, 2020, 2021], dtype=int64)

graphs = []
for i in years:
    graphs.append(graph_new[graph_new.year<=i][['node1', 'node2']])

def prependStringToFile(filename, fline):
    src=open(filename,"r") 
    #fline="newly added FIRST LINE\n"    #Prepending string 
    oline=src.readlines() 
    #Here, we prepend the string we want to on first line 
    oline.insert(0,fline) 
    src.close() 


    #We again open the file in WRITE mode  
    src=open(filename,"w") 
    src.writelines(oline) 
    src.close() 
    return True

for i, year in zip(graphs,years):
    i.to_csv(f'{year}icalp.lg', header = False, index = False)
    prependStringToFile(f'{year}icalp.lg', f'4669, {len(i)}, u, graph\n')

 len(set(graph_new.node1) | set(graph_new.node2))

4340

len(graph_new.year.unique())

48

Degree of separation

f = open("C:\\Users\\abezr\\eclipse-workspace\\ICALPG\\src\\degreeofseparation\\degreeofseparation.txt", "r")

degrees = np.array([float(i) for i in f])

degrees = pd.DataFrame(degrees.reshape((4,48)).T, columns = ['exact_value', 'k=15', 'k = 50', 'k = 100'])
degrees

	exact_value	k=15	k = 50	k = 100
0	1.000000	1.000000	1.000000	1.000000
1	1.333333	1.333333	1.326667	1.295000
2	1.600000	1.500000	1.585000	1.610000
3	1.600000	1.675000	1.570000	1.637500
4	2.142857	2.066667	2.142857	2.130000
5	2.194444	2.172222	2.187500	2.200833
6	2.688889	2.654321	2.673333	2.690370
7	2.841667	2.843333	2.846333	2.843500
8	3.442857	3.508333	3.468500	3.420875
9	3.220000	3.306250	3.218958	3.197187
10	3.681452	3.630538	3.614194	3.644258
11	3.790476	3.773714	3.832457	3.762343
12	5.344928	5.294815	5.318044	5.240933
13	5.370567	5.314894	5.568851	5.357915
14	5.663939	5.633103	5.567586	5.757310
15	6.365546	6.269577	6.354127	6.358651
16	6.241433	6.362884	6.197305	6.131649
17	6.447909	6.504639	6.494822	6.366521
18	7.120752	7.094299	7.125030	7.153249
19	7.090453	6.976687	7.108519	7.029053
20	8.951112	9.005111	8.905570	8.902779
21	9.860111	9.488475	10.066383	9.767349
22	9.911584	10.241028	9.957892	9.958329
23	10.119109	9.626304	10.025435	9.998054
24	11.559397	11.832440	11.497896	11.637998
25	11.396385	11.548862	11.338866	11.434624
26	10.807996	11.080381	10.695327	10.737771
27	10.022822	10.241491	10.025243	9.979722
28	9.912994	10.253569	9.960707	9.855426
29	9.676932	9.714838	9.679948	9.562926
30	9.172981	8.926493	9.073166	9.163981
31	9.248015	9.247559	9.292816	9.306347
32	10.376586	10.365492	10.432323	10.386816
33	10.068524	10.058157	10.090420	10.079735
34	9.472120	9.678124	9.441644	9.443433
35	8.426535	8.418148	8.332824	8.416645
36	8.163346	8.469149	8.265705	8.210588
37	8.014500	7.999064	8.013876	8.160187
38	7.954549	8.138074	7.972328	8.006432
39	7.661646	7.600042	7.743136	7.580158
40	7.528894	7.623006	7.429745	7.476004
41	7.444009	7.371030	7.409535	7.490338
42	7.262687	7.369164	7.271586	7.282966
43	7.154485	7.206434	7.141774	7.128678
44	6.918884	6.963822	6.904750	6.885549
45	6.843921	6.874061	6.922587	6.838542
46	6.784859	6.951537	6.869561	6.775201
47	6.696912	6.572705	6.762534	6.680183

degrees['year'] = years

degrees

	exact_value	k=15	k = 50	k = 100	year
0	1.000000	1.000000	1.000000	1.000000	1972
1	1.333333	1.333333	1.326667	1.295000	1974
2	1.600000	1.500000	1.585000	1.610000	1976
3	1.600000	1.675000	1.570000	1.637500	1977
4	2.142857	2.066667	2.142857	2.130000	1978
5	2.194444	2.172222	2.187500	2.200833	1979
6	2.688889	2.654321	2.673333	2.690370	1980
7	2.841667	2.843333	2.846333	2.843500	1981
8	3.442857	3.508333	3.468500	3.420875	1982
9	3.220000	3.306250	3.218958	3.197187	1983
10	3.681452	3.630538	3.614194	3.644258	1984
11	3.790476	3.773714	3.832457	3.762343	1985
12	5.344928	5.294815	5.318044	5.240933	1986
13	5.370567	5.314894	5.568851	5.357915	1987
14	5.663939	5.633103	5.567586	5.757310	1988
15	6.365546	6.269577	6.354127	6.358651	1989
16	6.241433	6.362884	6.197305	6.131649	1990
17	6.447909	6.504639	6.494822	6.366521	1991
18	7.120752	7.094299	7.125030	7.153249	1992
19	7.090453	6.976687	7.108519	7.029053	1993
20	8.951112	9.005111	8.905570	8.902779	1994
21	9.860111	9.488475	10.066383	9.767349	1995
22	9.911584	10.241028	9.957892	9.958329	1996
23	10.119109	9.626304	10.025435	9.998054	1997
24	11.559397	11.832440	11.497896	11.637998	1998
25	11.396385	11.548862	11.338866	11.434624	1999
26	10.807996	11.080381	10.695327	10.737771	2000
27	10.022822	10.241491	10.025243	9.979722	2001
28	9.912994	10.253569	9.960707	9.855426	2002
29	9.676932	9.714838	9.679948	9.562926	2003
30	9.172981	8.926493	9.073166	9.163981	2004
31	9.248015	9.247559	9.292816	9.306347	2005
32	10.376586	10.365492	10.432323	10.386816	2006
33	10.068524	10.058157	10.090420	10.079735	2007
34	9.472120	9.678124	9.441644	9.443433	2008
35	8.426535	8.418148	8.332824	8.416645	2009
36	8.163346	8.469149	8.265705	8.210588	2010
37	8.014500	7.999064	8.013876	8.160187	2011
38	7.954549	8.138074	7.972328	8.006432	2012
39	7.661646	7.600042	7.743136	7.580158	2013
40	7.528894	7.623006	7.429745	7.476004	2014
41	7.444009	7.371030	7.409535	7.490338	2015
42	7.262687	7.369164	7.271586	7.282966	2016
43	7.154485	7.206434	7.141774	7.128678	2017
44	6.918884	6.963822	6.904750	6.885549	2018
45	6.843921	6.874061	6.922587	6.838542	2019
46	6.784859	6.951537	6.869561	6.775201	2020
47	6.696912	6.572705	6.762534	6.680183	2021

px.line(degrees, x='year', y = degrees.columns,\
              title='degrees of separation')

Number of the new authors that joint the conference per year

graphs_new_ones = [len(set(graph_new[graph_new.year<=years[0]]['node1'])| set(graph_new[graph_new.year<=years[0]]['node1']))]
for i in range(1, years.shape[0]):
    curr_year = set(graph_new[graph_new.year<=years[i]]['node1'])| set(graph_new[graph_new.year<=years[i]]['node1'])
    prev_year = set(graph_new[graph_new.year<=years[i-1]]['node1']) | set(graph_new[graph_new.year<=years[i-1]]['node1'])
    graphs_new_ones.append(len(curr_year - prev_year))

plt.plot(years, graphs_new_ones)


fig = go.Figure()

fig.add_trace(go.Scatter(
    name=f"# new authors",
    mode="markers+lines", x=years,y =graphs_new_ones,
    marker_symbol="star"
))

fig.update_xaxes(showgrid=True, ticklabelmode="period")
fig.show()

Centrality analysis

data_centr_dict = {}
#data_centr = pd.DataFrame(columns = ['centricity_measure', 'year', f'{centr}_value', 'id'])
for centr in ['closeness_centrality', 'betweenness_centrality', 'degree_centrality','inverse_eccentricity' ]:
    a = pd.DataFrame(columns = ['year', 'number_in_top5', f'{centr}_value', 'id'])
    for year in years:
        _ = pd.DataFrame(columns = ['year', f'{centr}_value', 'id'])
        if centr =='closeness_centrality':
            filename = f'C:\\Users\\abezr\\eclipse-workspace\\ICALPG\\src\\centrality\\{year}clcentr_top_5.txt'
        else:
            filename = f'C:\\Users\\abezr\\eclipse-workspace\\ICALPG\\src\\centrality\\{year}{centr}_top_5.txt'
        src=open(filename,"r") 
        #fline="newly added FIRST LINE\n"    #Prepending string 
        oline=src.readlines()
        src.close()
        _[f'{centr}_value'] = np.array(oline[:5]).astype(float)
        _['id'] = np.array(oline[5:]).astype(float).astype(int)
        _['year'] = year
        _['number_in_top5'] = np.arange(1,6) 
        a = a.append(_, ignore_index=True)
        
            
    data_centr_dict[centr] = a
        

closeness_centrality = data_centr_dict['closeness_centrality']
closeness_centrality

	year	number_in_top5	closeness_centrality_value	id
0	1972	1	1.000000	4
1	1972	2	1.000000	5
2	1972	3	1.000000	6
3	1972	4	0.000000	4
4	1972	5	0.000000	4
...	...	...	...	...
235	2021	1	0.232857	2615
236	2021	2	0.224539	1176
237	2021	3	0.221603	484
238	2021	4	0.217983	1128
239	2021	5	0.216876	987

240 rows × 4 columns

authors_file = open('icalp_id_author.txt',  'r')
#authors.columns = ['id', 'name']
authors = {int(i.split(' ', maxsplit = 1)[0]): i.split(' ', maxsplit = 1)[1][:-1] for i in authors_file}
authors_file.close()
authors

{334: 'A. Goralciková',
 451: 'A. Prasad Sistla',
 235: 'A. Tang',
 803: 'A. Torrecillas',
 3599: 'A. V. Sreejith',
 561: 'A. W. Roscoe 0001',
 3499: 'Aaron Bernstein',
 4575: 'Aaron Potechin',
 2026: 'Aaron R. Bradley',
 2999: 'Aaron Roth 0001',
 3359: 'Aarthi Sundaram',
 1466: 'Abbas Edalat',
 3906: 'Abdallah Saffidine',
 700: 'Abha Moitra',
 1211: 'Abhiram G. Ranade',
 2533: 'Abhishek Jain 0002',
 3636: 'Abhradeep Thakurta',
 2651: 'Achim Blumensath',
 3228: 'Achim Passen',
 4011: 'Achiya Bar-On',
 1961: 'Adam D. Smith',
 3071: 'Adam Groce',
 4033: 'Adam Karczmarz',
 3421: 'Adam Kurpisz',
 1356: 'Adam L. Buchsbaum',
 2208: 'Adam Meyerson',
 4549: 'Adam Polak 0001',
 2557: 'Adam R. Klivans',
 3533: 'Adam Witkowski',
 4485: 'Adam Yedidia',
 1092: 'Adele A. Rescigno',
 1193: 'Adi Rosén',
 170: 'Adi Shamir',
 2797: 'Adin Rosenberg',
 4083: 'Aditi Singh',
 2975: 'Aditya Bhaskara',
 4382: 'Aditya Potukuchi',
 1010: 'Adnan Aziz',
 116: 'Adriaan van Wijngaarden',
 3559: 'Adrian Craciun',
 2649: 'Adrian Kosowski',
 3175: 'Adrian Neumann',
 270: 'Adrian Tang 0001',
 3845: 'Adriano Peron',
 2173: 'Aduri Pavan',
 1263: 'Afonso Ferreira',
 3364: 'Afshin Nikzad',
 1614: 'Aggelos Kiayias',
 907: 'Agostino Cortesi',
 3947: 'Ahmad Biniaz',
 847: 'Ahmed Bouajjani',
 566: 'Ahmed Saoudi',
 4150: 'Ainesh Bakshi',
 3072: 'Aishwarya Thiruvengadam',
 2361: 'Ajay Bansal',
 2362: 'Ajay Mallya',
 869: 'Ajit Agrawal',
 3674: 'Akanksha Agrawal',
 1920: 'Akash Nanavati',
 4222: 'Akbar Rafiey',
 2287: 'Akinori Kawachi',
 4165: 'Akira Yoshimizu',
 2426: 'Akshay Gaur',
 3342: 'Akshay Wadia',
 630: 'Alain Finkel',
 1927: 'Alain Frisch',
 1333: 'Alain Tapp',
 4338: 'Alan D. Logan',
 351: 'Alan G. Konheim',
 956: 'Alan Gibbons',
 2247: 'Alan Jeffrey',
 266: 'Alan L. Selman',
 889: 'Alan M. Frieze',
 425: 'Alan Mycroft',
 2765: 'Alan Schmitt',
 1897: 'Alan Skelley',
 1475: 'Albert Atserias',
 4441: 'Albert Gu',
 149: 'Albert R. Meyer',
 1133: 'Albert Rubio',
 910: 'Alberto Apostolico',
 64: 'Alberto Bertoni',
 1728: 'Alberto Del Lungo',
 4486: 'Alberto Dennunzio',
 1014: 'Alberto L. Sangiovanni-Vincentelli',
 604: 'Alberto Marchetti-Spaccamela',
 89: 'Alberto Martelli',
 3844: 'Alberto Molinari',
 362: 'Alberto Pettorossi',
 825: 'Albrecht Hoene',
 307: 'Aldo de Luca',
 4115: 'Alejandro Aguirre 0001',
 2766: 'Aleks Kissinger',
 3423: 'Aleksandar Nikolov',
 1632: 'Aleksandar Pekec',
 2658: 'Aleksander Madry',
 2792: 'Aleksandr Karbyshev',
 4360: 'Aleksandr Popov 0001',
 3150: 'Aleksandrs Belovs',
 1580: 'Aleksei V. Fishkin',
 4088: 'Aleksi Saarela',
 3877: 'Alessandro Chiesa',
 185: "Alessandro D'Atri",
 779: 'Alessandro Giacalone',
 2657: 'Alessandro Panconesi',
 1266: 'Alessandro Roncato',
 3760: 'Alessio Conte',
 2710: 'Alessio Orlandi',
 3397: 'Alex Borello',
 4501: 'Alex Brandts',
 4211: 'Alex Bredariol Grilo',
 65: 'Alex Dubinsky',
 1655: 'Alex Fabrikant',
 3872: 'Alex Galicki',
 1693: 'Alex K. Simpson',
 4578: 'Alex Steiger',
 1820: 'Alexander A. Ageev',
 773: 'Alexander A. Razborov',
 4141: 'Alexander A. Schwarzmann',
 4078: 'Alexander Conway',
 1182: 'Alexander E. Andreev',
 2661: 'Alexander Fanghänel',
 3269: 'Alexander Golovnev',
 2220: 'Alexander Golynski',
 4453: 'Alexander Göke',
 1853: 'Alexander Hall',
 1816: 'Alexander Kesselman',
 2920: 'Alexander Klaas',
 3127: 'Alexander Langer',
 3037: 'Alexander Lauser',
 795: 'Alexander Moshe Rabinovich',
 1843: 'Alexander Okhotin',
 4136: 'Alexander Ravsky',
 1667: 'Alexander Russell',
 3270: 'Alexander S. Kulikov',
 4201: 'Alexander Setzer',
 2586: 'Alexander Shen',
 4250: 'Alexander Skopalik',
 4633: 'Alexander Svozil',
 1364: 'Alexander Tiskin',
 2239: 'Alexander W. Dent',
 2401: 'Alexandr Andoni',
 2538: 'Alexandra Kolla',
 4243: 'Alexandra Lassota',
 3573: 'Alexandra Silva 0001',
 4620: 'Alexandre Goy 0002',
 168: 'Alexandre Ja. Dikovsky',
 3395: 'Alexandre Stauffer',
 4186: 'Alexandros Hollender',
 4323: 'Alexandru Cojocaru',
 4324: 'Alexandru Gheorghiu',
 4229: 'Alexandru I. Tomescu',
 2898: 'Alexey Gotsman',
 2195: 'Alexis C. Kaporis',
 154: 'Alfred V. Aho',
 894: 'Alfredo De Santis',
 1470: 'Ali Akhavi',
 4139: 'Ali Kemal Sinop',
 4193: 'Ali Sayyadi',
 2946: 'Ali Vakilian',
 4396: 'Alin Bostan',
 2879: 'Alina Ene',
 4610: 'Alireza Farhadi 0001',
 4636: 'Alireza Samadian',
 797: 'Alistair Sinclair',
 3018: 'Alistair Stewart',
 562: 'Allan Borodin',
 1212: 'Allan Cheng',
 2707: 'Allan Grønlund Jørgensen',
 648: 'Alok Aggarwal',
 162: 'Alon Itai',
 3531: 'Aloni Cohen',
 2286: 'Aloys Krieg',
 1668: 'Amalia Duch Brown',
 3770: 'Amanda Belleville',
 3653: 'Amaury Pouly',
 1889: 'Amela Prelic',
 3506: 'Amer E. Mouawad',
 3514: 'Amey Bhangale',
 4472: 'Ami Paz',
 1062: 'Amihood Amir',
 1858: 'Amin Coja-Oghlan',
 3480: 'Amin Gohari',
 2892: 'Amin Karbasi',
 3491: 'Amin Saberi',
 4438: 'Amineh Dadsetan',
 3142: 'Amir Abboud',
 583: 'Amir Averbuch',
 4274: 'Amir Kalev',
 1124: 'Amir M. Ben-Amram',
 3482: 'Amir Nayyeri',
 196: 'Amir Pnueli',
 2405: 'Amir Rothschild',
 2581: 'Amir Shpilka',
 3120: 'Amir Yehudayoff',
 342: 'Amiram Yehudai',
 3970: 'Amirbehshad Shahrasbi',
 4423: 'Amirmojtaba Sabour',
 1589: 'Amit Chakrabarti',
 3000: 'Amit Deshpande 0001',
 2021: 'Amit Kumar 0001',
 3230: 'Amit Kumar Dhar',
 2298: 'Amit Sahai',
 2735: 'Amitava Bhattacharya',
 3523: 'Amnon Ta-Shma',
 1562: 'Amos Beimel',
 1192: 'Amos Fiat',
 1866: 'Amos Korman',
 1229: 'Amotz Bar-Noy',
 1750: 'Amr Elmasry',
 3929: 'Amy Babay',
 883: 'Amy E. Zwarico',
 2944: 'Amélie Stainer',
 2119: 'An Braeken',
 1959: 'An Zhu',
 4100: 'Anand Louis',
 4030: 'Ananya Kumar',
 3483: 'Anastasios Sidiropoulos',
 2945: 'Anastasios Zouzias',
 3420: 'Anat Paskin-Cherniavsky',
 3873: 'Anatolii Kostrygin',
 3672: 'Anaël Grandjean',
 987: 'Anca Muscholl',
 3933: 'Anders Aamand',
 4308: 'Anders Martinsson',
 1280: 'Andrea Asperti',
 3392: 'Andrea Cerone',
 1321: 'Andrea Corradini 0001',
 1183: 'Andrea E. F. Clementi',
 1729: 'Andrea Frosini',
 3732: 'Andrea Lincoln',
 3761: 'Andrea Marino',
 2723: 'Andrea Montanari',
 1119: 'Andrea Pietracaprina',
 1643: 'Andrea Valente',
 2418: 'Andrea Vattani',
 1759: 'Andreas Björklund',
 2915: 'Andreas Cord-Landwehr',
 3485: 'Andreas Emil Feldmann',
 3417: 'Andreas Galanis',
 379: 'Andreas Goerdt',
 3524: 'Andreas Göbel 0001',
 2322: 'Andreas J. Winter 0002',
 1001: 'Andreas Jakoby',
 3225: 'Andreas Karrenbauer',
 1115: 'Andreas Parra',
 3196: 'Andreas Pieris',
 902: 'Andreas Podelski',
 1160: 'Andreas S. Schulz',
 4092: 'Andreas Schmid 0003',
 2728: 'Andreas Wiese',
 2104: 'Andreas Wotzlaw',
 1493: 'Andrei A. Bulatov',
 1494: 'Andrei A. Krokhin',
 2585: 'Andrei E. Romashchenko',
 4625: 'Andrei Graur',
 3122: 'Andrei Negoescu',
 3094: 'Andrei Stefanescu',
 3646: 'Andrei V. Gurtov',
 1220: 'Andrei Voronkov',
 888: 'Andrei Z. Broder',
 1691: 'Andrej Bauer',
 3858: 'Andrej Bogdanov',
 1924: 'Andrej Muchnik',
 4180: 'Andrej Sajenko',
 4370: 'Andrew Bassilakis',
 3084: 'Andrew Berns',
 866: 'Andrew Chi-Chih Yao',
 2757: 'Andrew Collins 0003',
 1415: 'Andrew D. Gordon',
 2880: 'Andrew Drucker',
 2988: 'Andrew Hughes',
 2399: 'Andrew M. Childs',
 1335: 'Andrew M. Pitts',
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 3771: 'David Soloveichik',
 2415: 'David Steurer',
 1795: 'David Van Campenhout',
 976: 'David W. Juedes',
 3944: 'David Wajc',
 1218: 'David Walker',
 2877: 'David Xiao',
 3185: 'David Zuckerman',
 1847: 'Davide Ancona',
 4034: 'Davide Bilò',
 892: 'Davide Sangiorgi',
 3932: 'Davis Issac',
 2768: 'Dawn Song',
 3522: 'Dean Doron',
 3609: 'Debajyoti Mondal',
 4655: 'Debarati Das 0001',
 4669: 'Debashmita Poddar',
 2660: 'Debmalya Panigrahi',
 2055: 'Debora Donato',
 4614: 'Deeksha Adil',
 2633: 'Deepak Chandra',
 395: 'Deepak Kapur',
 2161: 'Deeparnab Chakrabarty',
 2136: 'Dekel Tsur',
 939: 'Delia Kesner',
 51: 'Denis J. Kfoury',
 2795: 'Denis Kuperberg',
 1317: 'Denis Lugiez',
 4221: 'Denis Pankratov',
 256: 'Denis Thérien',
 2843: 'Dennis Komm',
 3923: 'Dennis Kraft',
 3677: 'Dennis Michaels',
 1100: 'Derek G. Corneil',
 476: 'Derick Wood',
 810: 'Desh Ranjan',
 2258: 'Detlef Kähler',
 686: 'Detlef Seese',
 218: 'Detlef Wotschke',
 3745: 'Devavrat Shah',
 323: 'Dexter Kozen',
 4644: 'Dhrumil Patel',
 2737: 'Dhruv Mubayi',
 3694: 'Di Wang 0005',
 2940: 'Diana Fischer',
 589: 'Didier Arquès',
 1188: 'Didier Caucal',
 482: 'Didier Ferment',
 4234: 'Didier Villevalois',
 ...}

closeness_centrality['author'] = closeness_centrality['id'].map(authors)
closeness_centrality

	year	number_in_top5	closeness_centrality_value	id	author
0	1972	1	1.000000	4	Aravind K. Joshi
1	1972	2	1.000000	5	Leon S. Levy
2	1972	3	1.000000	6	Masako Takahashi
3	1972	4	0.000000	4	Aravind K. Joshi
4	1972	5	0.000000	4	Aravind K. Joshi
...	...	...	...	...	...
235	2021	1	0.232857	2615	Ioannis Chatzigiannakis
236	2021	2	0.224539	1176	Piotr Indyk
237	2021	3	0.221603	484	Paul G. Spirakis
238	2021	4	0.217983	1128	Stefano Leonardi
239	2021	5	0.216876	987	Anca Muscholl

240 rows × 5 columns

fig = go.Figure()
for i in range(1,6):
    top1 = closeness_centrality[closeness_centrality.number_in_top5 == i]
    fig.add_trace(go.Scatter(
        name=f"Top {i}",
        mode="markers+lines", x=top1.year,y =top1.closeness_centrality_value,
        marker_symbol="star", hovertext = top1.author
    ))

fig.update_xaxes(showgrid=True, ticklabelmode="period")
fig.show()

# px.line(closeness_centrality, x='year', y = closeness_centrality.columns,\
#               title='closeness_centrality of top element')

fig = go.Figure()
for i in range(1,6):
    top1 = closeness_centrality[closeness_centrality.number_in_top5 == i]
    fig.add_trace(go.Scatter(
        name=f"Top {i}",
        mode="markers+lines", x=top1.year,y =top1.author,
        marker_symbol="star", hovertext = top1.closeness_centrality_value
    ))

fig.update_xaxes(showgrid=True, ticklabelmode="period")
fig.update_layout(
    title="Distribution of top autors closeness_centrality - wise", xaxis_title= 'year')#yaxis_title = 'authors'

fig.show()

for centr in data_centr_dict:
    print(centr)
    closeness_centrality = data_centr_dict[centr]
    closeness_centrality['author'] = closeness_centrality['id'].map(authors)
    import plotly.graph_objects as go


    fig = go.Figure()
    for i in range(1,6):
        top1 = closeness_centrality[closeness_centrality.number_in_top5 == i]
        fig.add_trace(go.Scatter(
            name=f"Top {i}",
            mode="markers+lines", x = top1.year,y = top1[f'{centr}_value'],
            marker_symbol="star", hovertext = top1.author
        ))
    fig.update_layout(
    title=f"Distribution of {centr} with years", yaxis_title = f'{centr}', xaxis_title= 'year')#


    fig.update_xaxes(showgrid=True, ticklabelmode="period")
    fig.show()
    
    
    fig = go.Figure()
    for i in range(1,6):
        top1 = closeness_centrality[closeness_centrality.number_in_top5 == i]
        fig.add_trace(go.Scatter(
            name=f"Top {i}",
            mode="markers+lines", x=top1.year,y =top1.author,
            marker_symbol="star", hovertext = top1[f'{centr}_value']
        ))

    fig.update_xaxes(showgrid=True, ticklabelmode="period")
    fig.update_layout(
        title=f"Distribution of top autors {centr} - wise", xaxis_title= 'year')#yaxis_title = 'authors'

    fig.show()

closeness_centrality

betweenness_centrality

degree_centrality

inverse_eccentricity